Unfavorable storage growth rate abatement

ABSTRACT

A method begins by a processing module determining, by a first storage unit, that a storage growth rate is unfavorable for the first storage unit over an estimated time frame of reallocating at least a portion of encoded data slices stored in the first storage unit to one or more additional storage units. The method continues with the processing module when the storage growth rate is unfavorable, selecting an unfavorable growth rate abatement approach such that estimated required storage capacity is less than available storage capacity of the first storage unit for the estimated time frame of the reallocation of the at least a portion of encoded data slices. The method continues with the processing module facilitating implementation of the unfavorable growth rate abatement approach.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION

Technical Field of the Invention

Aspects of this invention relate generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3A is a schematic block diagram of an embodiment of a decentralizedagreement module in accordance with the present invention;

FIG. 3B is a flowchart illustrating an example of selecting the resourcein accordance with the present invention;

FIG. 3C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 3D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory in accordance with the present invention;

FIGS. 4-5 are schematic block diagrams of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention; and

FIG. 6 is a flowchart illustrating an example embodiment of unfavorablegrowth rate abatement in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more non-publicintranet systems and/or public interne systems; and/or one or more localarea networks (LAN) and/or wide area networks (WAN). Hereafter, thedistributed computing system 10 may be interchangeably referred to as adispersed storage network (DSN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc. Hereafter, the DST execution unit may be interchangeably referredto as a storage unit and a set of DST execution units may beinterchangeably referred to as a set of storage units.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval, and data storage integrityverification. In accordance with these three primary functions, data canbe encoded (e.g., utilizing an information dispersal algorithm (IDA),utilizing a dispersed storage error encoding process), distributedlystored in physically different locations, and subsequently retrieved ina reliable and secure manner. Hereafter, distributedly stored may beinterchangeably referred to as dispersed stored. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width (e.g., anIDA width of the IDA) minus a decode threshold minus one) that mayresult from individual storage device (e.g., DST execution unit 36)failures and/or network equipment failures without loss of data andwithout the need for a redundant or backup copy. Further, thedistributed computing system 10 allows the data to be stored for anindefinite period of time without data loss and does so in a securemanner (e.g., the system is very resistant to attempts to gainunauthorized access the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block associated with a portion of an overall namespaceof the DSN) within memory of the DSTN module 22 for a user device, agroup of devices, or for public access and establishes per vaultdispersed storage (DS) error encoding parameters for a vault. The DSTNmanaging unit 18 may facilitate storage of DS error encoding parametersfor each vault of a plurality of vaults by updating registry informationfor the distributed computing system 10. The facilitating includesstoring updated system registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters for encoding and decoding) include data segmentinginformation (e.g., how many segments data (e.g., a file, a group offiles, a data block, etc.) is divided into), segment securityinformation (e.g., per segment encryption, compression, integritychecksum, etc.), error coding information (e.g., pillar/IDA width,decode threshold, read threshold, write threshold, etc.), slicinginformation (e.g., the number of encoded data slices that will becreated for each data segment); and slice security information (e.g.,per encoded data slice encryption, compression, integrity checksum,etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

Each slice name is unique to a corresponding encoded data slice andincludes multiple fields associated with the overall namespace of theDSN. For example, the fields may include a pillar number/pillar index, avault identifier, an object number uniquely associated with a particularfile for storage, and a data segment identifier of a plurality of datasegments, where the particular file is divided into the plurality ofdata segments. For example, each slice name of a set of slice namescorresponding to a set of encoded data slices that has been dispersedstorage error encoded from a common data segment varies only by entriesof the pillar number field as each share a common vault identifier, acommon object number, and a common data segment identifier.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data.With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.With respect to the DST management, it is substantially similar to theDST management to support distributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3A is a schematic block diagram of an embodiment of a decentralizedagreement module 350 that includes a set of deterministic functions 1-N,a set of normalizing functions 1-N, a set of scoring functions 1-N, anda ranking function 352. Each of the deterministic function, thenormalizing function, the scoring function, and the ranking function352, may be implemented utilizing the processing module 50 of FIG. 2.The decentralized agreement module 350 may be implemented utilizing anymodule and/or unit of a dispersed storage network (DSN). For example,the decentralized agreement module is implemented utilizing thedistributed storage and task (DST) client module 34 of FIG. 1.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSTclient module 34 of FIG. 1, a DST processing unit 16 of FIG. 1, a DSTintegrity processing unit 20 of FIG. 1, a DSTN managing unit 18 of FIG.1, a user device 12 of FIG. 1, and a user device 14 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, acyclic redundancy code function, hashing module of a number oflocations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 2 appends alocation ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function includes rank ordering each scorewith other scores of the set of scores 1-N, where a highest score isranked first. As such, a location associated with the highest score maybe considered a highest priority location for resource utilization(e.g., accessing, storing, retrieving, etc. the given asset of therequest). Having generated the ranked scoring information 358, thedecentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

FIG. 3B is a flowchart illustrating an example of selecting a resource.The method begins or continues at step 360 where a processing module(e.g., of a decentralized agreement module) receives a ranked scoringinformation request from a requesting entity with regards to a set ofcandidate resources. For each candidate resource, the method continuesat step 362 where the processing module performs a deterministicfunction on a location identifier (ID) of the candidate resource and anasset ID of the ranked scoring information request to produce an interimresult. As a specific example, the processing module combines the assetID and the location ID of the candidate resource to produce a combinedvalue and performs a hashing function on the combined value to producethe interim result.

For each interim result, the method continues at step 364 where theprocessing module performs a normalizing function on the interim resultto produce a normalized interim result. As a specific example, theprocessing module obtains a permutation value associated with thedeterministic function (e.g., maximum number of permutations of outputof the deterministic function) and divides the interim result by thepermutation value to produce the normalized interim result (e.g., with avalue between 0 and 1).

For each normalized interim result, the method continues at step 366where the processing module performs a scoring function on thenormalized interim result utilizing a location weight associated withthe candidate resource associated with the interim result to produce ascore of a set of scores. As a specific example, the processing moduledivides the location weight by a negative log of the normalized interimresult to produce the score.

The method continues at step 368 where the processing module rank ordersthe set of scores to produce ranked scoring information (e.g., ranking ahighest value first). The method continues at step 370 where theprocessing module outputs the ranked scoring information to therequesting entity. The requesting entity may utilize the ranked scoringinformation to select one location of a plurality of locations.

FIG. 3C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the distributed storage and task(DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1, and thedistributed storage and task network (DSTN) module 22 of FIG. 1.Hereafter, the DSTN module 22 may be interchangeably referred to as aDSN memory. The DST processing unit 16 includes a decentralizedagreement module 380 and the DST client module 34 of FIG. 1. Thedecentralized agreement module 380 be implemented utilizing thedecentralized agreement module 350 of FIG. 3A. The DSTN module 22includes a plurality of DST execution (EX) unit pools 1-P. Each DSTexecution unit pool includes one or more sites 1-S. Each site includesone or more DST execution units 1-N. Each DST execution unit may beassociated with at least one pillar of N pillars associated with aninformation dispersal algorithm (IDA), where a data segment is dispersedstorage error encoded using the IDA to produce one or more sets ofencoded data slices, and where each set includes N encoded data slicesand like encoded data slices (e.g., slice 3's) of two or more sets ofencoded data slices are included in a common pillar (e.g., pillar 3).Each site may not include every pillar and a given pillar may beimplemented at more than one site. Each DST execution unit includes aplurality of memories 1-M. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Hereafter, a DSTexecution unit may be referred to interchangeably as a storage unit anda set of DST execution units may be interchangeably referred to as a setof storage units and/or as a storage unit set.

The DSN functions to receive data access requests 382, select resourcesof at least one DST execution unit pool for data access, utilize theselected DST execution unit pool for the data access, and issue a dataaccess response 392 based on the data access. The selecting of theresources includes utilizing a decentralized agreement function of thedecentralized agreement module 380, where a plurality of locations areranked against each other. The selecting may include selecting onestorage pool of the plurality of storage pools, selecting DST executionunits at various sites of the plurality of sites, selecting a memory ofthe plurality of memories for each DST execution unit, and selectingcombinations of memories, DST execution units, sites, pillars, andstorage pools.

In an example of operation, the DST client module 34 receives the dataaccess request 382 from a requesting entity, where the data accessrequest 382 includes at least one of a store data request, a retrievedata request, a delete data request, a data name, and a requestingentity identifier (ID). Having received the data access request 382, theDST client module 34 determines a DSN address associated with the dataaccess request. The DSN address includes at least one of a source name(e.g., including a vault ID and an object number associated with thedata name), a data segment ID, a set of slice names, a plurality of setsof slice names. The determining includes at least one of generating(e.g., for the store data request) and retrieving (e.g., from a DSNdirectory, from a dispersed hierarchical index) based on the data name(e.g., for the retrieve data request).

Having determined the DSN address, the DST client module 34 selects aplurality of resource levels (e.g., DST EX unit pool, site, DSTexecution unit, pillar, memory) associated with the DSTN module 22. Thedetermining may be based on one or more of the data name, the requestingentity ID, a predetermination, a lookup, a DSN performance indicator,and interpreting an error message. For example, the DST client module 34selects the DST execution unit pool as a first resource level and a setof memory devices of a plurality of memory devices as a second resourcelevel based on a system registry lookup for a vault associated with therequesting entity.

Having selected the plurality of resource levels, the DST client module34, for each resource level, issues a ranked scoring information request384 to the decentralized agreement module 380 utilizing the DSN addressas an asset ID. The decentralized agreement module 380 performs thedecentralized agreement function based on the asset ID (e.g., the DSNaddress), identifiers of locations of the selected resource levels, andlocation weights of the locations to generate ranked scoring information386.

For each resource level, the DST client module 34 receives correspondingranked scoring information 386. Having received the ranked scoringinformation 386, the DST client module 34 identifies one or moreresources associated with the resource level based on the rank scoringinformation 386. For example, the DST client module 34 identifies a DSTexecution unit pool associated with a highest score and identifies a setof memory devices within DST execution units of the identified DSTexecution unit pool with a highest score.

Having identified the one or more resources, the DST client module 34accesses the DSTN module 22 based on the identified one or moreresources associated with each resource level. For example, the DSTclient module 34 issues resource access requests 388 (e.g., write slicerequests when storing data, read slice requests when recovering data) tothe identified DST execution unit pool, where the resource accessrequests 388 further identify the identified set of memory devices.Having accessed the DSTN module 22, the DST client module 34 receivesresource access responses 390 (e.g., write slice responses, read sliceresponses). The DST client module 34 issues the data access response 392based on the received resource access responses 390. For example, theDST client module 34 decodes received encoded data slices to reproducedata and generates the data access response 392 to include thereproduced data.

FIG. 3D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory. The method begins or continues at step 394where a processing module (e.g., of a distributed storage and task (DST)client module) receives a data access request from a requesting entity.The data access request includes one or more of a storage request, aretrieval request, a requesting entity identifier, and a data identifier(ID). The method continues at step 396 where the processing moduledetermines a DSN address associated with the data access request. Forexample, the processing module generates the DSN address for the storagerequest. As another example, the processing module performs a lookup forthe retrieval request based on the data identifier.

The method continues at step 398 where the processing module selects aplurality of resource levels associated with the DSN memory. Theselecting may be based on one or more of a predetermination, a range ofweights associated with available resources, a resource performancelevel, and a resource performance requirement level. For each resourcelevel, the method continues at step 400 where the processing moduledetermines ranked scoring information. For example, the processingmodule issues a ranked scoring information request to a decentralizedagreement module based on the DSN address and receives correspondingranked scoring information for the resource level, where thedecentralized agreement module performs a decentralized agreementprotocol function on the DSN address using the associated resourceidentifiers and resource weights for the resource level to produce theranked scoring information for the resource level.

For each resource level, the method continues at step 402 where theprocessing module selects one or more resources associated with theresource level based on the ranked scoring information. For example, theprocessing module selects a resource associated with a highest scorewhen one resource is required. As another example, the processing moduleselects a plurality of resources associated with highest scores when aplurality of resources are required.

The method continues at step 404 where the processing module accessesthe DSN memory utilizing the selected one or more resources for each ofthe plurality of resource levels. For example, the processing moduleidentifies network addressing information based on the selectedresources including one or more of a storage unit Internet protocoladdress and a memory device identifier, generates a set of encoded dataslice access requests based on the data access request and the DSNaddress, and sends the set of encoded data slice access requests to theDSN memory utilizing the identified network addressing information.

The method continues at step 406 where the processing module issues adata access response to the requesting entity based on one or moreresource access responses from the DSN memory. For example, theprocessing module issues a data storage status indicator when storingdata. As another example, the processing module generates the dataaccess response to include recovered data when retrieving data.

FIGS. 4-5 are schematic block diagrams of an embodiment of a dispersedstorage network (DSN) that includes unfavorable growth rate abatementfor a storage unit within the DSN.

FIG. 4 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes the distributed storage and task(DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1, and atleast two storage sets 1-2. The DST processing unit 16 includes adecentralized agreement module 420 and the DST client module 34 ofFIG. 1. The decentralized agreement module 420 may be implementedutilizing the decentralized agreement module 350 of FIG. 3A.

Each storage set includes a set of DST execution (EX) units. Forexample, the storage set 1 (424) includes DST execution units 1-1through 1-n and the storage set 2 (426) includes DST execution units 2-1through 2-n. Each DST execution unit includes a decentralized agreementmodule 420, the DST client module 34 of FIG. 1, and a memory 88. Thememory 88 may be implemented utilizing one or more of solid-statememory, magnetic disk drive memory, optical disk drive memory, etc. EachDST execution unit may be implemented utilizing the DST execution unit36 of FIG. 1. Hereafter, each DST execution unit may be interchangeablyreferred to as a storage unit and a storage set may be interchangeablyreferred to as a set of storage units. The DSN functions to manageavailable storage capacity of the DSN.

A Decentralized Agreement Protocol (DAP) may be employed to expand thestorage resources of a DSN memory. For example, new storage resourcesmay be provisioned, a resource map is then amended according to thisaddition, and the DAP is re-applied to every object/file/data/slice/etc.(the encoded data slice) in the system, to determine its new storagelocation. However, this process of recomputing the optimal location foreach encoded data slice, as well as conducting the transfer(reallocation), is not instantaneous, and may take some time. If therate at which resources are “reallocated” to the newly provisionedstorage resources is less than the rate at which new slices are beingingested by the original storage locations, then it is possible that thestorage resources of the existing storage units (e.g., DS) will becomeexhausted before the reallocation completes. For example, in a 15 PBsystem that is expanded to 20 PB, if the reallocation is sufficientlyslow, the system might fail with “out of capacity” errors when only 18PB have been written, as this number is greater than the storagecapacity of the original storage resources. However, it is still lessthan the total storage resources that have been provisioned, and it ispossible to avoid out of capacity errors in this situation by balancingthe priority and rate of incoming data growth (new writes—new deletes)vs. outgoing reallocation.

By prioritizing new deletes and outgoing reallocation, the originalstorage resources will not be in danger of running out of capacity. Ineffect, each storage unit in the system is calculating the growth rateas: (new writes—new deletes—outgoing reallocation), and multiplying itby the projected time of the reallocation, which is given by (totalamount of data to reallocate/reallocation rate). If the product of thesetwo values: net growth rate*reallocation time, is greater than theamount of remaining free capacity in the storage unit, then that storageunit is in danger of running out of space before the reallocationcompletes.

Example numbers the storage unit has control over is the rate at whichit processes new writes and the rate at which it processes reallocation.The storage unit may decide to adjust the ratio between these numberssuch that the product (above) is less than the remaining free capacityof the storage unit. When it does so, it guarantees that it will not runout of capacity prior to the completion of the reallocation.

In an example of operation of the managing of the available storagecapacity, reallocating encoded data slices to another storage unit is inaccordance with updated weighting factors. A storage unit determinesthat a storage growth rate is unfavorable for the storage unit over anestimated time frame of the reallocation of the encoded data slices. Theestimated time frame=amount of data to reallocate divided by thereallocation rate. For example, the DST execution unit 1-1 indicates theunfavorable storage growth rate when (estimated number of new encodeddata slices—estimated number of deleted encoded data slices—number ofencoded data slices for reallocation)>available storage capacity of theDST execution unit 1-1 (e.g., of the memory 88) over the timeframe ofthe reallocation, where the DST execution unit 1-1 receives to slices 1as the new slices and sends transfer slices 1 to the DST execution unit2-1 as the slices for reallocation.

FIG. 5 is schematic block diagrams of an embodiment of a dispersedstorage network (DSN) that includes unfavorable growth rate abatementfor a storage unit within the DSN. When the storage growth rate isunfavorable, the storage unit selects an unfavorable growth rateabatement approach such that estimated requires storage capacity is lessthan the available storage capacity of the storage unit. The approachesincludes lowering a rate of storing slices, raising a rate of deletingslices, and raising a rate of the reallocating of the slices. Theselecting may be based on one or more of a prioritization request, aschedule, a DSN activity level, and a maximum estimated reallocationrate. For example, the DST client module 34 of the DST execution unit1-1 determines to slow down storing of data slices (e.g., new slices 1)and speed up the reallocation of the transfer slices (e.g., slices 1) tothe DST execution unit 2-1 based on an updated calculation of theestimated requires storage capacity and the available storage capacityof the memory 88 of the DST execution unit 1-1.

Having selected the unfavorable growth rate abatement approach, thestorage unit facilitates application of the unfavorable growth rateabatement approach. For example, the DST client module 34 of the DSTexecution unit 1-1 slows down the storing of new data slices (e.g., newslices 1), speeds up deleting of previously allocated encoded dataslices, and/or speeds up the reallocation of the transfer slices (e.g.,slices 1) to the DST execution unit 2-1. In one example embodiment, thefacilitating includes throttling (slowing down) the writing of newencoded data slices by a percentage (e.g., 20%, 50%, etc.).

FIG. 6 is a flowchart illustrating an example embodiment of unfavorablegrowth rate abatement. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-2, 3A-D, 4-5, and also FIG. 6.

The method begins or continues with a step 602 where a processing module(e.g., of a storage unit), while reallocating encoded data slices from astorage unit to another storage unit in accordance with updatedweighting factors of a distributed agreement protocol function,determines that a storage growth rate is unfavorable for the storageunit over an estimated time frame of the reallocation of the encodeddata slices. For example, the processing module indicates unfavorablewhen (estimated number of new encoded data slices—estimated number ofdeleted encoded data slices—number of encoded data slices forreallocation)>available storage capacity of the storage unit.

When the storage growth rate is unfavorable, the method continues atstep 604 where the processing module selects an unfavorable growth rateabatement approach such that the estimated requires storage capacity isless than available storage capacity of the storage unit for theestimated timeframe of the reallocation of the encoded data slices. Theselecting may be based on one or more of a prioritization request, aschedule, a DSN activity level, and a maximum estimated reallocationrate.

The method continues at step 606 where the processing module facilitatesapplication of the unfavorable growth rate abatement approach. Forexample, the processing module speeds up reallocation of the encodeddata slices, speeds up deletion of reallocated encoded data slices,and/or slows down (e.g., throttling by a percentage) writing of newencoded data slices to the storage unit.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other devices. In addition, at least one memory section(e.g., a non-transitory computer readable storage medium) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of the dispersed storagenetwork (DSN), cause the one or more computing devices to perform any orall of the method steps described above.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: determining, by a first storage unit, thata storage growth rate is unfavorable for the first storage unit over anestimated time frame of reallocating at least a portion of encoded dataslices stored in the first storage unit to one or more additionalstorage units; when the storage growth rate is unfavorable, selecting anunfavorable growth rate abatement approach such that an estimatedrequired storage capacity is less than available storage capacity of thefirst storage unit for the estimated time frame of the reallocation ofthe at least a portion of encoded data slices; and facilitatingimplementation of the unfavorable growth rate abatement approach.
 2. Themethod of claim 1, wherein the reallocating is in accordance withupdated weighting factors of a distributed agreement protocol function.3. The method of claim 1, wherein the growth rate is indicatedunfavorable when an estimated number of new encoded data slices minus anestimated number of deleted encoded data slices minus a number ofencoded data slices for reallocation is greater than an availablestorage capacity of the storage unit.
 4. The method of claim 1, whereinthe unfavorable growth rate abatement approach is based on one or moreof: a prioritization request, a schedule, a DSN activity level, or amaximum estimated reallocation rate.
 5. The method of claim 1, whereinthe unfavorable growth rate abatement approach is based on one or moreof: lower a rate of storing new encoded data slices, raise a rate ofdeleting encoded data slices, or raising a rate of reallocating encodeddata slices.
 6. The method of claim 1, wherein the facilitating includesone or more of: speeding up a rate of reallocating of the at least aportion of encoded data slices or slowing down writing of new encodeddata slices.
 7. The method of claim 1, wherein the facilitating includesspeeding up a rate of deletions, in the first storage unit, ofreallocated encoded data slices.
 8. The method of claim 1, wherein thefacilitating includes throttling writing new encoded data slices by apercentage.
 9. A non-transitory computer readable storage mediumcomprises: at least one memory section that stores operationalinstructions that, when executed by one or more processing modules ofone or more computing devices of a dispersed storage network (DSN),causes the one or more computing devices to: determine, by a firststorage unit, that a storage growth rate is unfavorable for the firststorage unit over an estimated time frame of reallocating at least aportion of encoded data slices stored in the first storage unit to oneor more additional storage units; when the storage growth rate isunfavorable, select an unfavorable growth rate abatement approach suchthat an estimated required storage capacity is less than availablestorage capacity of the first storage unit for the estimated time frameof the reallocation of the at least a portion of encoded data slices;and facilitate implementation of the unfavorable growth rate abatementapproach.
 10. The non-transitory computer readable storage medium ofclaim 9 further comprises: the at least one memory section storesfurther operational instructions that, when executed by the one or moreprocessing modules, causes the one or more computing devices of the DSNto: reallocate in accordance with updated weighting factors of adistributed agreement protocol function.
 11. The non-transitory computerreadable storage medium of claim 9 further comprises: the at least onememory section stores further operational instructions that, whenexecuted by the one or more processing modules, causes the one or morecomputing devices of the DSN to: indicate the unfavorable growth ratewhen an estimated number of new encoded data slices minus an estimatednumber of deleted encoded data slices minus a number of encoded dataslices for reallocation is greater than an available storage capacity ofthe storage unit.
 12. The non-transitory computer readable storagemedium of claim 9 further comprises: the at least one memory sectionstores further operational instructions that, when executed by the oneor more processing modules, causes the one or more computing devices ofthe DSN to: base the unfavorable growth rate abatement approach on oneor more of: a prioritization request, a schedule, a DSN activity level,or a maximum estimated reallocation rate.
 13. The non-transitorycomputer readable storage medium of claim 9 further comprises: the atleast one memory section stores further operational instructions that,when executed by the one or more processing modules, causes the one ormore computing devices of the DSN to: base the unfavorable growth rateabatement approach on one or more of: lower a rate of storing newencoded data slices, raise a rate of deleting encoded data slices, orraising a rate of reallocating encoded data slices.
 14. Thenon-transitory computer readable storage medium of claim 9 furthercomprises: the at least one memory section stores further operationalinstructions that, when executed by the one or more processing modules,causes the one or more computing devices of the DSN to: facilitate basedon one or more of: speeding up a rate of reallocating of the at least aportion of encoded data slices or slowing down writing of new encodeddata slices.
 15. The non-transitory computer readable storage medium ofclaim 9 further comprises: the at least one memory section storesfurther operational instructions that, when executed by the one or moreprocessing modules, causes the one or more computing devices of the DSNto: facilitate based on speeding up a rate of deletions, in the firststorage unit, of reallocated encoded data slices.
 16. A computing deviceof a group of computing devices of a dispersed storage network (DSN),the computing device comprises: an interface; a local memory; and aprocessing module operably coupled to the interface and the localmemory, wherein the processing module functions to: determine, by afirst storage unit, that a storage growth rate is unfavorable for thefirst storage unit over an estimated time frame of reallocating at leasta portion of encoded data slices stored in the first storage unit to oneor more additional storage units; when the storage growth rate isunfavorable, select an unfavorable growth rate abatement approach suchthat an estimated required storage capacity is less than availablestorage capacity of the first storage unit for the estimated time frameof the reallocation of the at least a portion of encoded data slices;and facilitate implementation of the unfavorable growth rate abatementapproach.
 17. The computing device of claim 16, wherein the growth rateis indicated unfavorable when an estimated number of new encoded dataslices minus an estimated number of deleted encoded data slices minus anumber of encoded data slices for reallocation is greater than anavailable storage capacity of the storage unit.
 18. The computing deviceof claim 16, wherein the unfavorable growth rate abatement approach isbased on one or more of: a prioritization request, a schedule, a DSNactivity level, or a maximum estimated reallocation rate.
 19. Thecomputing device of claim 16, wherein the unfavorable growth rateabatement approach is based on one or more of: lower a rate of storingnew encoded data slices, raise a rate of deleting encoded data slices,or raising a rate of reallocating encoded data slices.
 20. The computingdevice of claim 16, wherein the facilitating includes one or more of:speeding up a rate of reallocating of the at least a portion of encodeddata slices, speeding up a rate of deletions, in the first storage unit,of reallocated encoded data slices, or slowing down writing of newencoded data slices.